"""Project token supply under mint/burn schedules."""
from __future__ import annotations
from datetime import datetime, timezone
from typing import Any
TOOL_META: dict[str, Any] = {
"name": "token_supply_modeler",
"description": "Projects circulating supply month by month with mint and burn events.",
"inputSchema": {
"type": "object",
"properties": {
"initial_supply": {"type": "number"},
"mint_schedule": {"type": "array", "items": {"type": "object"}},
"burn_events": {"type": "array", "items": {"type": "object"}},
"months_to_project": {"type": "integer"},
},
"required": [
"initial_supply",
"mint_schedule",
"burn_events",
"months_to_project",
],
},
"outputSchema": {
"type": "object",
"properties": {
"status": {"type": "string"},
"data": {"type": "object"},
"timestamp": {"type": "string"},
},
},
}
def token_supply_modeler(
initial_supply: float,
mint_schedule: list[dict[str, Any]],
burn_events: list[dict[str, Any]],
months_to_project: int,
**_: Any,
) -> dict[str, Any]:
"""Produce the projected supply curve."""
try:
if initial_supply <= 0:
raise ValueError("initial_supply must be positive")
if months_to_project <= 0:
raise ValueError("months_to_project must be positive")
mint_map = {int(item["month"]): float(item.get("amount", 0)) for item in mint_schedule}
burn_map = {int(item["month"]): float(item.get("amount", 0)) for item in burn_events}
supply_curve = []
supply = initial_supply
total_minted = 0.0
total_burned = 0.0
for month in range(1, months_to_project + 1):
minted = mint_map.get(month, 0.0)
burned = burn_map.get(month, 0.0)
total_minted += minted
total_burned += burned
supply = max(supply + minted - burned, 0)
supply_curve.append(
{
"month": month,
"supply": round(supply, 2),
"minted": minted,
"burned": burned,
}
)
net_inflation_pct = round(((supply - initial_supply) / initial_supply) * 100, 4)
data = {
"supply_curve": supply_curve,
"final_supply": round(supply, 2),
"total_minted": round(total_minted, 2),
"total_burned": round(total_burned, 2),
"net_inflation_pct": net_inflation_pct,
}
return {
"status": "success",
"data": data,
"timestamp": datetime.now(timezone.utc).isoformat(),
}
except Exception as exc:
_log_lesson("token_supply_modeler", str(exc))
return {
"status": "error",
"data": {"error": str(exc)},
"timestamp": datetime.now(timezone.utc).isoformat(),
}
def _log_lesson(skill_name: str, error: str) -> None:
with open("logs/lessons.md", "a", encoding="utf-8") as handle:
handle.write(f"- [{datetime.now(timezone.utc).isoformat()}] {skill_name}: {error}\n")
